Classification of single extracellular vesicles in a double nanohole optical tweezer for cancer detection

Abstract

A major challenge in cancer prognostics is finding early biomarkers that can accurately identify cancer. Circulating tumor cells are rare and circulating tumor DNA can not provide information about the originating cell. Extracellular vesicles (EVs) contain cell specific information, are abundant in fluids, and have unique properties between cancerous and non-cancerous. Fluorescence measurements have limitations from intrinsic fluorescent background signals, photobleaching, non-specific labelling, and EV structural modifications. Here, we demonstrate a label-free approach to classification of 3 different EVs, derived from non-malignant, non-invasive cancerous, and invasive cancerous cell lines. Using double nanohole optical tweezers, the scattering from single trapped EVs is measured, and using a 1D convolutional neural network, we are able to classify the time series optical signal into its respective EV class with greater than 90% accuracy.

Keywords

• cancer detection
• optical tweezers
• extracellular vesicles
• label-free
• single molecule sensors
• machine learning